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Private AI platform · Local control · Security-first roadmap

SephiraLLM — Self-hosted AI for sensitive environments

A configurable, local-first AI platform for organizations that need private document analysis, security workflows and compliance support without sending sensitive data to cloud LLMs.

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Local-first Self-hosted Model-agnostic No mandatory cloud Enterprise-ready direction
The problem

AI adoption is blocked when sensitive data cannot leave your environment.

Many organizations want to use AI, but cannot send sensitive data to cloud LLMs because of client confidentiality, GDPR, regulated environments, internal policies, contractual restrictions, security requirements or air-gapped deployments.

SephiraLLM is positioned for controlled, realistic pilots in these environments. It supports privacy-sensitive workflows without making exaggerated compliance guarantees or claiming certifications that are not in place.

What SephiraLLM is

A configurable private AI platform designed around deployment control, provider choice and security assumptions.

Local-first platform

Runs locally or on-premise and uses local/open-weight models where appropriate for the deployment and data sensitivity.

Provider abstraction

Ollama is the first supported runtime, not the product boundary. The architecture is designed so models and runtimes can change.

Use-case profiles

Use-case profiles define behavior, policies and capabilities; they do not hardcode a specific model. Models are configurable per deployment and per use case.

Security from the beginning

Designed with security documentation, threat modeling and clear operator assumptions from the start rather than as an afterthought.

Use-case profiles

Profiles configure behavior and policies for a business workflow while remaining separate from the model/runtime provider.

A

Confidential Document Assistant

For lawyers, tax advisors, consultants and regulated teams that need local document Q&A, summarization, clause extraction and source citations.

B

Security Analyst Assistant

For SecOps, IT security and infrastructure teams that need local analysis of logs, scanner output, CVEs, firewall snippets or incident notes.

C

Compliance Copilot

For ISO 27001, NIS2, DORA, GDPR and internal audit support: mapping, evidence preparation and gap analysis support, not legal advice.

D

Internal Knowledge Base

For private company runbooks, policies, onboarding docs, operational procedures and internal documentation with local retrieval and citations.

E

Secure Developer Assistant

For confidential codebases where code should not be sent to cloud assistants. Local code explanation, secure review support, and test or documentation drafting.

F

Air-gapped / Offline AI Appliance

A deployment profile that can be combined with other use cases for OT, industrial, critical infrastructure or contractual no-cloud environments.

G

Customer Support Knowledge Assistant

An on-prem assistant for sensitive ticket history, internal support knowledge and operational procedures that should stay within the organization.

Architecture principles

Business-friendly design principles for private AI deployments, with roadmap items clearly separated from pilot assumptions.

Designed into the platform direction

  • Model-agnostic design with provider/runtime abstraction.
  • Deployment profiles for local laptop, Docker Compose, on-prem VM and offline scenarios.
  • No telemetry by default, no mandatory telemetry and no cloud dependency required by the product direction.
  • Security-first roadmap with threat modeling, documented assumptions and operator handover.

Enterprise deployment path

Planned enterprise capabilities include PostgreSQL-backed deployments, OIDC/LDAP, RBAC, TLS hardening, backup/restore, auditability as a product capability, offline updates and later Kubernetes deployment. These are roadmap and customer-integration concerns, not blanket claims that every feature ships today.

Commercial pilot

Private AI Pilot

A focused pilot to validate one local AI use case in a controlled environment.

Suggested deliverables

  • use-case scoping workshop
  • local/self-hosted deployment concept
  • selected use-case profile
  • model/runtime recommendation
  • threat model and security assumptions
  • operator handover
  • next-step roadmap
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